{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "colab": {
      "name": "Action_cnews.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "5YvRDeGtiGtS",
        "outputId": "e3fbee2b-a4aa-456d-e3a2-39b617cba34e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "import os\n",
        "os.chdir(\"/content/drive/My Drive/Colab Notebooks/Bi-IV/cnews文本分类\")"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yb8gYEFPiDdc"
      },
      "source": [
        "# Action1_cnews 中文文本分类\n",
        "\n",
        "由清华大学根据新浪新闻RSS订阅频道2005-2011年间的历史数据筛选过滤生成     训练集 50000     验证集 5000     测试集 10000     词汇（字） 5000     10个分类，包括：'体育', '财经', '房产', '家居', '教育', '科技', '时尚', '时政', '游戏', '娱乐'"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BJ-WkGe9iDdk"
      },
      "source": [
        "# 设置数据目录\n",
        "train_file = 'cnews.train.small.txt'\n",
        "test_file = 'cnews.test.txt'\n",
        "val_file = 'cnews.val.txt'\n",
        "vocab_file = 'cnews.vocab.txt'"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v8W42fcuiDdd"
      },
      "source": [
        "import torch\n",
        "from torch import nn\n",
        "from model import TextRNN\n",
        "from cnews_loader import read_vocab,read_category,process_file\n",
        "from torch import optim"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "outputs_hidden": true
        },
        "id": "bW_I02jIiDdp",
        "outputId": "0fc24011-5957-4681-b3e6-502ebb761e77",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        }
      },
      "source": [
        "# 获取文本的类别及其对应id的字典\n",
        "categories, cat_to_id = read_category()\n",
        "print('categories={},\\ncat_to_id={}'.format(categories,cat_to_id))\n",
        "# 获取训练文本中所有出现过的字及其所对应的id\n",
        "# word_to_id = {'<PAD>': 0, '，': 1, '的': 2, '。': 3, '一': 4, '是': 5......}\n",
        "words, word_to_id = read_vocab('cnews.vocab.txt') \n",
        "print('words={},\\nword_to_id={}'.format(words[:108],dict(list(word_to_id.items())[:10])))\n",
        "# 获取训练数据每个字的id和对应标签的one-hot形式\n",
        "# x_train.shape = (1000,600),y_train.shape = (1000,10)\n",
        "x_train, y_train = process_file(train_file, word_to_id, cat_to_id, 600)\n",
        "print('x_train.shape={},\\ny_train.shape={}'.format(x_train.shape,y_train.shape))\n",
        "x_val, y_val = process_file(val_file, word_to_id, cat_to_id, 600)\n",
        "print('x_val.shape={},\\ny_val.shape={}'.format(x_val.shape,y_val.shape))\n"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "categories=['体育', '财经', '房产', '家居', '教育', '科技', '时尚', '时政', '游戏', '娱乐'],\n",
            "cat_to_id={'体育': 0, '财经': 1, '房产': 2, '家居': 3, '教育': 4, '科技': 5, '时尚': 6, '时政': 7, '游戏': 8, '娱乐': 9}\n",
            "words=['<PAD>', '，', '的', '。', '一', '是', '在', '0', '有', '不', '了', '中', '1', '人', '大', '、', '国', '', '2', '这', '上', '为', '个', '“', '”', '年', '学', '时', '我', '地', '和', '以', '到', '出', '来', '会', '行', '发', '：', '对', '们', '要', '生', '家', '他', '能', '也', '业', '金', '3', '成', '可', '分', '多', '现', '5', '就', '场', '新', '后', '于', '下', '日', '经', '市', '前', '过', '方', '得', '作', '月', '最', '开', '房', '》', '《', '高', '9', '8', '.', '而', '比', '公', '4', '说', ')', '将', '(', '都', '资', 'e', '6', '基', '用', '面', '产', '还', '自', '者', '本', '之', '美', '很', '同', '', '7', '部', '进'],\n",
            "word_to_id={'<PAD>': 0, '，': 1, '的': 2, '。': 3, '一': 4, '是': 5, '在': 6, '0': 7, '有': 8, '不': 9}\n",
            "x_train.shape=(10000, 600),\n",
            "y_train.shape=(10000, 10)\n",
            "x_val.shape=(5000, 600),\n",
            "y_val.shape=(5000, 10)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1m4-bnGZiDd3"
      },
      "source": [
        "import torch.utils.data as Data\n",
        "import numpy as np\n",
        "# 设置GPU\n",
        "cuda = torch.device('cuda')\n",
        "x_train, y_train = torch.LongTensor(x_train), torch.Tensor(y_train).to(dtype=torch.int64)\n",
        "x_val, y_val = torch.LongTensor(x_val), torch.Tensor(y_val).to(dtype=torch.int64)\n",
        "\n",
        "# .TensorDataset检查x_train和y_train第一维是否相同，相同则继续\n",
        "# (1000,600)(1000,10)，assert all(tensors[0].size(0) == tensor.size(0) for tensor in tensors)\n",
        "train_dataset = Data.TensorDataset(x_train, y_train)  \n",
        "val_dataset = Data.TensorDataset(x_val, y_val)\n",
        "#\n",
        "train_loader = Data.DataLoader(dataset = train_dataset, batch_size=256, shuffle=True)\n",
        "val_loader = Data.DataLoader(dataset = val_dataset, batch_size=256)"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dfcgffg1OT0J",
        "outputId": "758b9902-b5ef-48c4-960a-f38e1074afbc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "source": [
        "x_train,y_train"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(tensor([[1609,  659,   56,  ...,    9,  311,    3],\n",
              "         [   2,  101,   16,  ..., 1168,    3,   24],\n",
              "         [ 465,  855,  521,  ...,  116,  136,   85],\n",
              "         ...,\n",
              "         [   0,    0,    0,  ...,  603, 1791, 1474],\n",
              "         [   0,    0,    0,  ...,   96,  630,    3],\n",
              "         [ 314, 1525,  332,  ...,   10,    3,   24]]),\n",
              " tensor([[1, 0, 0,  ..., 0, 0, 0],\n",
              "         [1, 0, 0,  ..., 0, 0, 0],\n",
              "         [1, 0, 0,  ..., 0, 0, 0],\n",
              "         ...,\n",
              "         [0, 1, 0,  ..., 0, 0, 0],\n",
              "         [0, 1, 0,  ..., 0, 0, 0],\n",
              "         [0, 1, 0,  ..., 0, 0, 0]]))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HBTWOvnXiDdv"
      },
      "source": [
        "loss_list = []\n",
        "loss_epoch_list = []\n",
        "accuracy_epoch_list = []\n",
        "accuracy_mean_list = []   # 每个epoch计算一次accuracy平均\n",
        "loss_epochMean_list = []   # 每个epoch计算一次loss平均\n",
        "EPOCH = 100\n",
        "def train():\n",
        "    model = TextRNN().cuda()\n",
        "    # 定义损失函数\n",
        "    Loss = nn.MultiLabelSoftMarginLoss() # 多类别（multi-class）多分类（multi-classification）的 Hinge 损失\n",
        "    optimizer = optim.Adam(model.parameters(),lr=0.001)\n",
        "    \n",
        "    best_val_acc = 0\n",
        "    \n",
        "    for epoch in range(EPOCH):\n",
        "        print('***********************************************************')\n",
        "        print('epoch=',epoch)\n",
        "        # 分批训练\n",
        "        temp_loss = 0\n",
        "        accuracy_mean = 0\n",
        "        for step, (x_batch, y_batch) in enumerate(train_loader):\n",
        "            \n",
        "            x = x_batch.cuda()  #[128, 600]\n",
        "            y = y_batch.cuda() # [128, 10]\n",
        "            # 前向传播\n",
        "            out = model(x) # [128, 10]\n",
        "            loss = Loss(out, y)\n",
        "            # loss_list.append(loss) # 保存loss，方便画图\n",
        "            temp_loss += loss\n",
        "            print('loss=', loss)\n",
        "            # 反向传播\n",
        "            optimizer.zero_grad()\n",
        "            loss.backward()\n",
        "            optimizer.step()\n",
        "            accuracy = np.mean((torch.argmax(out,1) == torch.argmax(y,1)).cpu().numpy())\n",
        "            accuracy_mean += accuracy\n",
        "            print('accuracy=', accuracy)\n",
        "        # print(step,temp_loss)\n",
        "        # accuracy_epoch_list.append(accuracy)\n",
        "        accuracy_mean_list.append(accuracy_mean/(step+1))\n",
        "        # loss_epoch_list.append(loss) # 保存loss，方便画图\n",
        "        loss_epochMean_list.append(temp_loss/(step+1))\n",
        "\n",
        "        if (epoch+1)%5==0:\n",
        "            # 模型验证\n",
        "            for step, (x_batch, y_batch) in enumerate(val_loader):\n",
        "                x = x_batch.cuda()\n",
        "                y = y_batch.cuda()\n",
        "                # 前向传播\n",
        "                out = model(x)\n",
        "                accuracy = np.mean((torch.argmax(out,1) == torch.argmax(y,1)).cpu().numpy())\n",
        "                if accuracy > best_val_acc:\n",
        "                    torch.save(model,'model.pkl')\n",
        "                    best_val_acc = accuracy\n",
        "                    print('model.pkl saved')\n",
        "                    print('accuracy=',accuracy)\n",
        "                    "
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o3QGXMrulxkN",
        "outputId": "ed5d7233-ada5-4445-ec2b-78013873c1bd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "train()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "***********************************************************\n",
            "epoch= 0\n",
            "loss= tensor(0.7344, device='cuda:0', grad_fn=<MeanBackward0>)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/container.py:117: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n",
            "  input = module(input)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\u001b[1;30;43m流式输出内容被截断，只能显示最后 5000 行内容。\u001b[0m\n",
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            "loss= tensor(0.7131, device='cuda:0', grad_fn=<MeanBackward0>)\n",
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            "loss= tensor(0.7121, device='cuda:0', grad_fn=<MeanBackward0>)\n",
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            "loss= tensor(0.7130, device='cuda:0', grad_fn=<MeanBackward0>)\n",
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            "loss= tensor(0.7145, device='cuda:0', grad_fn=<MeanBackward0>)\n",
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            "loss= tensor(0.7130, device='cuda:0', grad_fn=<MeanBackward0>)\n",
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            "loss= tensor(0.7117, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7120, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7130, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7112, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7097, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4375\n",
            "loss= tensor(0.7088, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.453125\n",
            "loss= tensor(0.7113, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7108, device='cuda:0', grad_fn=<MeanBackward0>)\n",
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            "loss= tensor(0.7106, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7251, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3125\n",
            "***********************************************************\n",
            "epoch= 40\n",
            "loss= tensor(0.7095, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4375\n",
            "loss= tensor(0.7146, device='cuda:0', grad_fn=<MeanBackward0>)\n",
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            "accuracy= 0.375\n",
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            "accuracy= 0.34765625\n",
            "loss= tensor(0.7123, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7085, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41796875\n",
            "loss= tensor(0.7090, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7136, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7088, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.43359375\n",
            "loss= tensor(0.7078, device='cuda:0', grad_fn=<MeanBackward0>)\n",
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            "accuracy= 0.359375\n",
            "loss= tensor(0.7125, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7096, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7119, device='cuda:0', grad_fn=<MeanBackward0>)\n",
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            "loss= tensor(0.7091, device='cuda:0', grad_fn=<MeanBackward0>)\n",
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            "loss= tensor(0.7109, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7099, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41796875\n",
            "loss= tensor(0.7106, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7133, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7140, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37890625\n",
            "loss= tensor(0.7128, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7145, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7139, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.375\n",
            "loss= tensor(0.7150, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7120, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7135, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7122, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7125, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7089, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4453125\n",
            "loss= tensor(0.7155, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.36328125\n",
            "loss= tensor(0.7109, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7140, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.375\n",
            "loss= tensor(0.7133, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.375\n",
            "loss= tensor(0.7029, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.5\n",
            "***********************************************************\n",
            "epoch= 41\n",
            "loss= tensor(0.7158, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.328125\n",
            "loss= tensor(0.7126, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7137, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.359375\n",
            "loss= tensor(0.7082, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4453125\n",
            "loss= tensor(0.7116, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7120, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7109, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7187, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.34765625\n",
            "loss= tensor(0.7155, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.35546875\n",
            "loss= tensor(0.7164, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.33984375\n",
            "loss= tensor(0.7116, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7114, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7121, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7128, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7140, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.33984375\n",
            "loss= tensor(0.7098, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7146, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3359375\n",
            "loss= tensor(0.7084, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4375\n",
            "loss= tensor(0.7182, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.359375\n",
            "loss= tensor(0.7099, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7093, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7111, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7106, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7103, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7116, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7095, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7108, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7077, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.46484375\n",
            "loss= tensor(0.7131, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37109375\n",
            "loss= tensor(0.7105, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7089, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4296875\n",
            "loss= tensor(0.7118, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7122, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7094, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7158, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.32421875\n",
            "loss= tensor(0.7172, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.35546875\n",
            "loss= tensor(0.7134, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7194, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.30859375\n",
            "loss= tensor(0.7107, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.6914, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.5625\n",
            "***********************************************************\n",
            "epoch= 42\n",
            "loss= tensor(0.7091, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7106, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7100, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7158, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3515625\n",
            "loss= tensor(0.7115, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7142, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3671875\n",
            "loss= tensor(0.7135, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7125, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7146, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.33984375\n",
            "loss= tensor(0.7088, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.45703125\n",
            "loss= tensor(0.7115, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7098, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7131, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7175, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.34375\n",
            "loss= tensor(0.7153, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.33203125\n",
            "loss= tensor(0.7123, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.359375\n",
            "loss= tensor(0.7116, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7085, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7102, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7133, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7124, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7118, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7080, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.43359375\n",
            "loss= tensor(0.7117, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7090, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44921875\n",
            "loss= tensor(0.7073, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4453125\n",
            "loss= tensor(0.7122, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7130, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7132, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7113, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7174, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.32421875\n",
            "loss= tensor(0.7121, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7141, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.359375\n",
            "loss= tensor(0.7155, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.359375\n",
            "loss= tensor(0.7171, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.31640625\n",
            "loss= tensor(0.7159, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.33203125\n",
            "loss= tensor(0.7071, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.45703125\n",
            "loss= tensor(0.7112, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7141, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.375\n",
            "loss= tensor(0.7334, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.1875\n",
            "***********************************************************\n",
            "epoch= 43\n",
            "loss= tensor(0.7115, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.375\n",
            "loss= tensor(0.7144, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7124, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37109375\n",
            "loss= tensor(0.7139, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37890625\n",
            "loss= tensor(0.7115, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7096, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7132, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3671875\n",
            "loss= tensor(0.7094, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7096, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41796875\n",
            "loss= tensor(0.7138, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7108, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7127, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.36328125\n",
            "loss= tensor(0.7124, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37109375\n",
            "loss= tensor(0.7103, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7129, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.375\n",
            "loss= tensor(0.7103, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7125, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7093, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41796875\n",
            "loss= tensor(0.7136, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37890625\n",
            "loss= tensor(0.7155, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7095, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41796875\n",
            "loss= tensor(0.7136, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37890625\n",
            "loss= tensor(0.7140, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37109375\n",
            "loss= tensor(0.7128, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7122, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7155, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3671875\n",
            "loss= tensor(0.7140, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3671875\n",
            "loss= tensor(0.7087, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7136, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.359375\n",
            "loss= tensor(0.7108, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.43359375\n",
            "loss= tensor(0.7117, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41796875\n",
            "loss= tensor(0.7113, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7117, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37890625\n",
            "loss= tensor(0.7124, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7132, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3671875\n",
            "loss= tensor(0.7103, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44921875\n",
            "loss= tensor(0.7119, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7140, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7132, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.6977, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.5\n",
            "***********************************************************\n",
            "epoch= 44\n",
            "loss= tensor(0.7128, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7100, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7110, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7132, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7134, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7140, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.359375\n",
            "loss= tensor(0.7102, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7129, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7136, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3671875\n",
            "loss= tensor(0.7130, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37890625\n",
            "loss= tensor(0.7148, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3671875\n",
            "loss= tensor(0.7120, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7102, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7089, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7101, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7106, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44921875\n",
            "loss= tensor(0.7140, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7066, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.43359375\n",
            "loss= tensor(0.7092, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.453125\n",
            "loss= tensor(0.7120, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3671875\n",
            "loss= tensor(0.7129, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7075, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44140625\n",
            "loss= tensor(0.7111, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7154, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.359375\n",
            "loss= tensor(0.7106, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7105, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7130, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7135, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7169, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3046875\n",
            "loss= tensor(0.7163, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.32421875\n",
            "loss= tensor(0.7109, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7151, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3515625\n",
            "loss= tensor(0.7111, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7141, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.36328125\n",
            "loss= tensor(0.7128, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7144, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37890625\n",
            "loss= tensor(0.7113, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7150, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.375\n",
            "loss= tensor(0.7089, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44140625\n",
            "loss= tensor(0.7189, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3125\n",
            "***********************************************************\n",
            "epoch= 45\n",
            "loss= tensor(0.7152, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.375\n",
            "loss= tensor(0.7123, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7163, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.36328125\n",
            "loss= tensor(0.7096, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7148, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.36328125\n",
            "loss= tensor(0.7096, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7115, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7047, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4453125\n",
            "loss= tensor(0.7152, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3515625\n",
            "loss= tensor(0.7148, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.359375\n",
            "loss= tensor(0.7115, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7125, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7146, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.34375\n",
            "loss= tensor(0.7148, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7128, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7078, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.43359375\n",
            "loss= tensor(0.7087, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41796875\n",
            "loss= tensor(0.7124, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7136, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.375\n",
            "loss= tensor(0.7111, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7143, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7174, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.33203125\n",
            "loss= tensor(0.7126, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.375\n",
            "loss= tensor(0.7116, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7098, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44140625\n",
            "loss= tensor(0.7143, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3515625\n",
            "loss= tensor(0.7123, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.43359375\n",
            "loss= tensor(0.7074, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4375\n",
            "loss= tensor(0.7086, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7115, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7097, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7148, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3671875\n",
            "loss= tensor(0.7089, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7118, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7116, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.43359375\n",
            "loss= tensor(0.7125, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7107, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.43359375\n",
            "loss= tensor(0.7135, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.359375\n",
            "loss= tensor(0.7126, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7246, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3125\n",
            "***********************************************************\n",
            "epoch= 46\n",
            "loss= tensor(0.7100, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41796875\n",
            "loss= tensor(0.7122, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37109375\n",
            "loss= tensor(0.7111, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7133, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7101, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7132, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37109375\n",
            "loss= tensor(0.7146, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.36328125\n",
            "loss= tensor(0.7090, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4453125\n",
            "loss= tensor(0.7125, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7126, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7127, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37890625\n",
            "loss= tensor(0.7158, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3125\n",
            "loss= tensor(0.7129, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7166, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3515625\n",
            "loss= tensor(0.7107, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7131, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7097, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41796875\n",
            "loss= tensor(0.7140, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7128, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37890625\n",
            "loss= tensor(0.7140, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3671875\n",
            "loss= tensor(0.7101, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44140625\n",
            "loss= tensor(0.7102, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41796875\n",
            "loss= tensor(0.7115, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7114, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7095, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7062, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.453125\n",
            "loss= tensor(0.7109, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7130, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7131, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.375\n",
            "loss= tensor(0.7143, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.359375\n",
            "loss= tensor(0.7081, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7143, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3671875\n",
            "loss= tensor(0.7143, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7148, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.34375\n",
            "loss= tensor(0.7106, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7116, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7121, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7153, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37109375\n",
            "loss= tensor(0.7103, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.43359375\n",
            "loss= tensor(0.7151, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4375\n",
            "***********************************************************\n",
            "epoch= 47\n",
            "loss= tensor(0.7156, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.328125\n",
            "loss= tensor(0.7114, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41796875\n",
            "loss= tensor(0.7103, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7128, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7111, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7109, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7152, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3515625\n",
            "loss= tensor(0.7093, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7144, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.375\n",
            "loss= tensor(0.7119, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7105, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4375\n",
            "loss= tensor(0.7158, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3515625\n",
            "loss= tensor(0.7123, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37890625\n",
            "loss= tensor(0.7130, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.375\n",
            "loss= tensor(0.7128, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4375\n",
            "loss= tensor(0.7117, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7116, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7122, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3671875\n",
            "loss= tensor(0.7157, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.33984375\n",
            "loss= tensor(0.7107, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7111, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7156, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.34765625\n",
            "loss= tensor(0.7104, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7124, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7129, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37109375\n",
            "loss= tensor(0.7127, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7096, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7107, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7133, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37890625\n",
            "loss= tensor(0.7134, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37109375\n",
            "loss= tensor(0.7079, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4375\n",
            "loss= tensor(0.7107, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4296875\n",
            "loss= tensor(0.7125, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7126, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3671875\n",
            "loss= tensor(0.7109, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7119, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7122, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7123, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7086, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41796875\n",
            "loss= tensor(0.7182, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.25\n",
            "***********************************************************\n",
            "epoch= 48\n",
            "loss= tensor(0.7116, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37890625\n",
            "loss= tensor(0.7088, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44921875\n",
            "loss= tensor(0.7073, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.46484375\n",
            "loss= tensor(0.7112, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7149, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3671875\n",
            "loss= tensor(0.7102, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7156, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3359375\n",
            "loss= tensor(0.7102, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7114, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7096, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7106, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41796875\n",
            "loss= tensor(0.7139, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7141, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.34765625\n",
            "loss= tensor(0.7082, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.43359375\n",
            "loss= tensor(0.7089, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4296875\n",
            "loss= tensor(0.7141, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37890625\n",
            "loss= tensor(0.7121, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37109375\n",
            "loss= tensor(0.7089, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7123, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7164, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3359375\n",
            "loss= tensor(0.7137, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3515625\n",
            "loss= tensor(0.7141, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7129, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.375\n",
            "loss= tensor(0.7162, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3203125\n",
            "loss= tensor(0.7105, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7099, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7138, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3671875\n",
            "loss= tensor(0.7112, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7126, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7123, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41796875\n",
            "loss= tensor(0.7155, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.36328125\n",
            "loss= tensor(0.7098, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4296875\n",
            "loss= tensor(0.7121, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7139, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3671875\n",
            "loss= tensor(0.7124, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7109, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7096, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4296875\n",
            "loss= tensor(0.7095, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7148, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.35546875\n",
            "loss= tensor(0.7143, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.375\n",
            "***********************************************************\n",
            "epoch= 49\n",
            "loss= tensor(0.7098, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41796875\n",
            "loss= tensor(0.7076, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44140625\n",
            "loss= tensor(0.7137, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.375\n",
            "loss= tensor(0.7137, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37109375\n",
            "loss= tensor(0.7159, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3359375\n",
            "loss= tensor(0.7120, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7115, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7089, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4375\n",
            "loss= tensor(0.7148, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.34375\n",
            "loss= tensor(0.7129, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7172, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.34375\n",
            "loss= tensor(0.7105, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7080, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44140625\n",
            "loss= tensor(0.7098, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7089, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44140625\n",
            "loss= tensor(0.7110, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7116, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7104, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41796875\n",
            "loss= tensor(0.7113, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7118, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7143, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37109375\n",
            "loss= tensor(0.7138, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7150, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3515625\n",
            "loss= tensor(0.7106, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7178, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.359375\n",
            "loss= tensor(0.7102, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7163, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.34765625\n",
            "loss= tensor(0.7130, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3671875\n",
            "loss= tensor(0.7205, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.296875\n",
            "loss= tensor(0.7089, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7107, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7105, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7126, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7165, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.33984375\n",
            "loss= tensor(0.7079, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.43359375\n",
            "loss= tensor(0.7094, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44921875\n",
            "loss= tensor(0.7126, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3671875\n",
            "loss= tensor(0.7141, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37890625\n",
            "loss= tensor(0.7092, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7149, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.375\n",
            "***********************************************************\n",
            "epoch= 50\n",
            "loss= tensor(0.7115, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7131, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7112, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7128, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3671875\n",
            "loss= tensor(0.7110, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7109, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7106, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7129, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.375\n",
            "loss= tensor(0.7140, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.375\n",
            "loss= tensor(0.7134, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3515625\n",
            "loss= tensor(0.7136, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.35546875\n",
            "loss= tensor(0.7164, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.36328125\n",
            "loss= tensor(0.7111, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7158, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37109375\n",
            "loss= tensor(0.7147, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7132, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.375\n",
            "loss= tensor(0.7140, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3515625\n",
            "loss= tensor(0.7136, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7113, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7104, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7097, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41796875\n",
            "loss= tensor(0.7126, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7132, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7096, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7086, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4453125\n",
            "loss= tensor(0.7105, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7100, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4296875\n",
            "loss= tensor(0.7123, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7104, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7103, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7152, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37890625\n",
            "loss= tensor(0.7123, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7122, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7111, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7118, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7113, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7116, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7131, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.359375\n",
            "loss= tensor(0.7097, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44140625\n",
            "loss= tensor(0.6905, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.75\n",
            "***********************************************************\n",
            "epoch= 51\n",
            "loss= tensor(0.7144, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3515625\n",
            "loss= tensor(0.7137, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7159, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.33203125\n",
            "loss= tensor(0.7121, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.359375\n",
            "loss= tensor(0.7144, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.36328125\n",
            "loss= tensor(0.7097, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.43359375\n",
            "loss= tensor(0.7153, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.34765625\n",
            "loss= tensor(0.7109, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37890625\n",
            "loss= tensor(0.7095, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.43359375\n",
            "loss= tensor(0.7083, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7091, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4375\n",
            "loss= tensor(0.7122, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7094, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44921875\n",
            "loss= tensor(0.7124, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7082, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4453125\n",
            "loss= tensor(0.7163, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3671875\n",
            "loss= tensor(0.7133, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7085, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.46484375\n",
            "loss= tensor(0.7113, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7140, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7137, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7095, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4296875\n",
            "loss= tensor(0.7110, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41796875\n",
            "loss= tensor(0.7164, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.34765625\n",
            "loss= tensor(0.7117, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7138, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7151, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3671875\n",
            "loss= tensor(0.7110, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7104, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7099, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7116, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7120, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7130, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7114, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7176, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.35546875\n",
            "loss= tensor(0.7111, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7109, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41796875\n",
            "loss= tensor(0.7086, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4375\n",
            "loss= tensor(0.7093, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44140625\n",
            "loss= tensor(0.6997, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4375\n",
            "***********************************************************\n",
            "epoch= 52\n",
            "loss= tensor(0.7098, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7150, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.35546875\n",
            "loss= tensor(0.7088, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4375\n",
            "loss= tensor(0.7126, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7163, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.36328125\n",
            "loss= tensor(0.7113, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7115, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7096, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7140, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3671875\n",
            "loss= tensor(0.7119, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3671875\n",
            "loss= tensor(0.7096, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7150, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.359375\n",
            "loss= tensor(0.7133, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37890625\n",
            "loss= tensor(0.7105, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4375\n",
            "loss= tensor(0.7131, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37109375\n",
            "loss= tensor(0.7088, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7086, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7123, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7134, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37109375\n",
            "loss= tensor(0.7129, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37109375\n",
            "loss= tensor(0.7134, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37890625\n",
            "loss= tensor(0.7104, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7126, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3671875\n",
            "loss= tensor(0.7087, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44140625\n",
            "loss= tensor(0.7089, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4453125\n",
            "loss= tensor(0.7135, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37109375\n",
            "loss= tensor(0.7092, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7143, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.359375\n",
            "loss= tensor(0.7094, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41796875\n",
            "loss= tensor(0.7114, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7164, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3359375\n",
            "loss= tensor(0.7075, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.47265625\n",
            "loss= tensor(0.7130, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7109, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7139, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7079, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4375\n",
            "loss= tensor(0.7152, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3671875\n",
            "loss= tensor(0.7183, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3203125\n",
            "loss= tensor(0.7113, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.43359375\n",
            "loss= tensor(0.7061, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.5\n",
            "***********************************************************\n",
            "epoch= 53\n",
            "loss= tensor(0.7128, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3671875\n",
            "loss= tensor(0.7118, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37890625\n",
            "loss= tensor(0.7129, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7107, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7111, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7127, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.375\n",
            "loss= tensor(0.7148, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3671875\n",
            "loss= tensor(0.7111, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7090, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.43359375\n",
            "loss= tensor(0.7068, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4609375\n",
            "loss= tensor(0.7140, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.36328125\n",
            "loss= tensor(0.7120, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.375\n",
            "loss= tensor(0.7077, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44921875\n",
            "loss= tensor(0.7135, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37109375\n",
            "loss= tensor(0.7086, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44140625\n",
            "loss= tensor(0.7147, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37890625\n",
            "loss= tensor(0.7132, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3671875\n",
            "loss= tensor(0.7109, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7125, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7141, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7111, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7134, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7108, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7092, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.43359375\n",
            "loss= tensor(0.7125, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.36328125\n",
            "loss= tensor(0.7104, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7047, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4609375\n",
            "loss= tensor(0.7145, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3515625\n",
            "loss= tensor(0.7166, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.34765625\n",
            "loss= tensor(0.7104, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7098, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41796875\n",
            "loss= tensor(0.7151, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.375\n",
            "loss= tensor(0.7179, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3515625\n",
            "loss= tensor(0.7117, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7106, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7108, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4375\n",
            "loss= tensor(0.7106, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7132, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7104, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7031, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.5625\n",
            "***********************************************************\n",
            "epoch= 54\n",
            "loss= tensor(0.7119, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7097, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4453125\n",
            "loss= tensor(0.7101, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44921875\n",
            "loss= tensor(0.7158, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3515625\n",
            "loss= tensor(0.7121, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7089, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4375\n",
            "loss= tensor(0.7143, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.375\n",
            "loss= tensor(0.7169, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.33984375\n",
            "loss= tensor(0.7119, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7163, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.34765625\n",
            "loss= tensor(0.7109, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4296875\n",
            "loss= tensor(0.7112, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7127, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7135, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7105, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7114, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7155, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3515625\n",
            "loss= tensor(0.7071, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7104, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7091, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4296875\n",
            "loss= tensor(0.7078, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.43359375\n",
            "loss= tensor(0.7113, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7132, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.36328125\n",
            "loss= tensor(0.7155, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.35546875\n",
            "loss= tensor(0.7134, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37109375\n",
            "loss= tensor(0.7106, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7117, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7098, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7117, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7082, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7113, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.453125\n",
            "loss= tensor(0.7116, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7129, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7113, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7114, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41796875\n",
            "loss= tensor(0.7112, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41796875\n",
            "loss= tensor(0.7126, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7119, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7088, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4296875\n",
            "loss= tensor(0.7208, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.375\n",
            "***********************************************************\n",
            "epoch= 55\n",
            "loss= tensor(0.7130, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7142, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.375\n",
            "loss= tensor(0.7118, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7120, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7117, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7116, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7120, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7145, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3671875\n",
            "loss= tensor(0.7108, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37890625\n",
            "loss= tensor(0.7141, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.359375\n",
            "loss= tensor(0.7147, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.375\n",
            "loss= tensor(0.7086, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4296875\n",
            "loss= tensor(0.7113, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7123, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7099, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4375\n",
            "loss= tensor(0.7123, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7068, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4375\n",
            "loss= tensor(0.7077, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.484375\n",
            "loss= tensor(0.7044, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.46484375\n",
            "loss= tensor(0.7143, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37890625\n",
            "loss= tensor(0.7077, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4765625\n",
            "loss= tensor(0.7121, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7103, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7132, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37890625\n",
            "loss= tensor(0.7112, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7128, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37109375\n",
            "loss= tensor(0.7141, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7110, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7139, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7153, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.33203125\n",
            "loss= tensor(0.7118, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7116, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7090, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7139, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7140, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7145, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.36328125\n",
            "loss= tensor(0.7096, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4296875\n",
            "loss= tensor(0.7133, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3671875\n",
            "loss= tensor(0.7128, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7176, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3125\n",
            "***********************************************************\n",
            "epoch= 56\n",
            "loss= tensor(0.7146, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37109375\n",
            "loss= tensor(0.7113, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7127, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7123, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7101, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7179, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.32421875\n",
            "loss= tensor(0.7134, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37109375\n",
            "loss= tensor(0.7089, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44140625\n",
            "loss= tensor(0.7119, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7137, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37890625\n",
            "loss= tensor(0.7080, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44140625\n",
            "loss= tensor(0.7120, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7102, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7087, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7141, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7051, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.48046875\n",
            "loss= tensor(0.7107, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7144, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7110, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41796875\n",
            "loss= tensor(0.7152, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3359375\n",
            "loss= tensor(0.7128, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7110, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.375\n",
            "loss= tensor(0.7098, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7114, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7141, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.359375\n",
            "loss= tensor(0.7117, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7086, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4296875\n",
            "loss= tensor(0.7112, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7120, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7117, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4296875\n",
            "loss= tensor(0.7109, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7134, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7151, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7082, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4296875\n",
            "loss= tensor(0.7115, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7102, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7128, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7080, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44140625\n",
            "loss= tensor(0.7115, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7088, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4375\n",
            "***********************************************************\n",
            "epoch= 57\n",
            "loss= tensor(0.7115, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7112, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7089, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4375\n",
            "loss= tensor(0.7084, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4453125\n",
            "loss= tensor(0.7126, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7091, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7110, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7113, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7127, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7141, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7088, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.46875\n",
            "loss= tensor(0.7108, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7128, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37109375\n",
            "loss= tensor(0.7086, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7114, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7145, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3359375\n",
            "loss= tensor(0.7107, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7096, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7098, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7155, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.33984375\n",
            "loss= tensor(0.7129, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.35546875\n",
            "loss= tensor(0.7127, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7084, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44140625\n",
            "loss= tensor(0.7141, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3515625\n",
            "loss= tensor(0.7122, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7126, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7111, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7116, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7125, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7151, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.35546875\n",
            "loss= tensor(0.7108, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4296875\n",
            "loss= tensor(0.7092, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7136, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37890625\n",
            "loss= tensor(0.7096, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.43359375\n",
            "loss= tensor(0.7140, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7135, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37890625\n",
            "loss= tensor(0.7132, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.375\n",
            "loss= tensor(0.7116, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7125, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37109375\n",
            "loss= tensor(0.7108, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.5\n",
            "***********************************************************\n",
            "epoch= 58\n",
            "loss= tensor(0.7116, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37109375\n",
            "loss= tensor(0.7129, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.359375\n",
            "loss= tensor(0.7103, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7103, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7092, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.43359375\n",
            "loss= tensor(0.7124, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37890625\n",
            "loss= tensor(0.7109, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7122, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7136, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7126, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7109, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7123, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7111, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7106, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.453125\n",
            "loss= tensor(0.7136, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37109375\n",
            "loss= tensor(0.7108, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41796875\n",
            "loss= tensor(0.7106, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7121, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7106, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44140625\n",
            "loss= tensor(0.7114, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7134, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7083, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.43359375\n",
            "loss= tensor(0.7103, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7083, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7102, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7097, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41796875\n",
            "loss= tensor(0.7130, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7120, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7146, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.359375\n",
            "loss= tensor(0.7163, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.36328125\n",
            "loss= tensor(0.7143, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.375\n",
            "loss= tensor(0.7108, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37890625\n",
            "loss= tensor(0.7108, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7119, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7119, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37890625\n",
            "loss= tensor(0.7089, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.43359375\n",
            "loss= tensor(0.7126, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7111, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4296875\n",
            "loss= tensor(0.7138, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7199, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3125\n",
            "***********************************************************\n",
            "epoch= 59\n",
            "loss= tensor(0.7127, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7081, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4375\n",
            "loss= tensor(0.7119, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7139, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37109375\n",
            "loss= tensor(0.7107, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7081, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4296875\n",
            "loss= tensor(0.7093, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7127, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7136, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7123, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7124, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37890625\n",
            "loss= tensor(0.7139, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3671875\n",
            "loss= tensor(0.7113, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7127, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37890625\n",
            "loss= tensor(0.7088, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7118, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41796875\n",
            "loss= tensor(0.7081, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44140625\n",
            "loss= tensor(0.7080, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44921875\n",
            "loss= tensor(0.7133, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3671875\n",
            "loss= tensor(0.7130, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.36328125\n",
            "loss= tensor(0.7127, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7096, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7150, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3515625\n",
            "loss= tensor(0.7145, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.359375\n",
            "loss= tensor(0.7083, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44140625\n",
            "loss= tensor(0.7132, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7089, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.43359375\n",
            "loss= tensor(0.7109, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37890625\n",
            "loss= tensor(0.7102, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4375\n",
            "loss= tensor(0.7112, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7137, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7119, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7128, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7116, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7123, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37109375\n",
            "loss= tensor(0.7133, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.375\n",
            "loss= tensor(0.7089, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7099, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7118, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7192, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.375\n",
            "***********************************************************\n",
            "epoch= 60\n",
            "loss= tensor(0.7088, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7144, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37109375\n",
            "loss= tensor(0.7134, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7073, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44921875\n",
            "loss= tensor(0.7091, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7108, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7067, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4609375\n",
            "loss= tensor(0.7158, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.36328125\n",
            "loss= tensor(0.7113, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7145, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.375\n",
            "loss= tensor(0.7148, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37890625\n",
            "loss= tensor(0.7115, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7142, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.36328125\n",
            "loss= tensor(0.7111, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7115, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7075, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4375\n",
            "loss= tensor(0.7125, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7100, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.43359375\n",
            "loss= tensor(0.7151, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.36328125\n",
            "loss= tensor(0.7124, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7156, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3359375\n",
            "loss= tensor(0.7126, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37890625\n",
            "loss= tensor(0.7086, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4296875\n",
            "loss= tensor(0.7129, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7089, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7105, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7103, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41796875\n",
            "loss= tensor(0.7123, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7127, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7147, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.35546875\n",
            "loss= tensor(0.7105, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7137, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7130, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37109375\n",
            "loss= tensor(0.7134, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.35546875\n",
            "loss= tensor(0.7085, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4375\n",
            "loss= tensor(0.7146, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.375\n",
            "loss= tensor(0.7117, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4296875\n",
            "loss= tensor(0.7094, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4453125\n",
            "loss= tensor(0.7085, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41796875\n",
            "loss= tensor(0.7246, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.25\n",
            "***********************************************************\n",
            "epoch= 61\n",
            "loss= tensor(0.7109, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7114, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7115, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7092, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.453125\n",
            "loss= tensor(0.7124, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37109375\n",
            "loss= tensor(0.7110, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37890625\n",
            "loss= tensor(0.7111, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7113, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37109375\n",
            "loss= tensor(0.7095, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7110, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7092, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.43359375\n",
            "loss= tensor(0.7139, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3671875\n",
            "loss= tensor(0.7090, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7137, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7079, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4453125\n",
            "loss= tensor(0.7137, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37890625\n",
            "loss= tensor(0.7133, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3671875\n",
            "loss= tensor(0.7132, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7128, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7152, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.34765625\n",
            "loss= tensor(0.7134, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7123, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37890625\n",
            "loss= tensor(0.7097, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4296875\n",
            "loss= tensor(0.7165, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.33984375\n",
            "loss= tensor(0.7124, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41796875\n",
            "loss= tensor(0.7049, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.484375\n",
            "loss= tensor(0.7098, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7110, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7073, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4453125\n",
            "loss= tensor(0.7090, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7155, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.359375\n",
            "loss= tensor(0.7099, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4296875\n",
            "loss= tensor(0.7135, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37890625\n",
            "loss= tensor(0.7127, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.375\n",
            "loss= tensor(0.7106, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7136, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7150, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7096, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7141, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37890625\n",
            "loss= tensor(0.6855, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.625\n",
            "***********************************************************\n",
            "epoch= 62\n",
            "loss= tensor(0.7144, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.35546875\n",
            "loss= tensor(0.7090, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7103, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7150, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3671875\n",
            "loss= tensor(0.7135, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7104, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7137, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.36328125\n",
            "loss= tensor(0.7110, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7145, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3671875\n",
            "loss= tensor(0.7139, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7147, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.36328125\n",
            "loss= tensor(0.7125, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7112, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7128, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.375\n",
            "loss= tensor(0.7090, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7095, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.43359375\n",
            "loss= tensor(0.7072, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44140625\n",
            "loss= tensor(0.7092, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7160, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37109375\n",
            "loss= tensor(0.7088, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4375\n",
            "loss= tensor(0.7132, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7106, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41796875\n",
            "loss= tensor(0.7146, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3671875\n",
            "loss= tensor(0.7071, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4375\n",
            "loss= tensor(0.7148, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.36328125\n",
            "loss= tensor(0.7135, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7105, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4296875\n",
            "loss= tensor(0.7119, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4375\n",
            "loss= tensor(0.7057, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.45703125\n",
            "loss= tensor(0.7130, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37890625\n",
            "loss= tensor(0.7107, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7150, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37109375\n",
            "loss= tensor(0.7098, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7126, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37890625\n",
            "loss= tensor(0.7089, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.43359375\n",
            "loss= tensor(0.7084, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7123, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7144, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.34765625\n",
            "loss= tensor(0.7111, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7171, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3125\n",
            "***********************************************************\n",
            "epoch= 63\n",
            "loss= tensor(0.7122, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37890625\n",
            "loss= tensor(0.7110, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41796875\n",
            "loss= tensor(0.7137, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7115, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7135, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3671875\n",
            "loss= tensor(0.7091, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7107, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7099, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7077, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.453125\n",
            "loss= tensor(0.7104, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7107, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4375\n",
            "loss= tensor(0.7126, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7105, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7136, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7112, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7092, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4375\n",
            "loss= tensor(0.7087, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.43359375\n",
            "loss= tensor(0.7109, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7082, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7156, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3671875\n",
            "loss= tensor(0.7127, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37890625\n",
            "loss= tensor(0.7105, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7134, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7093, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.453125\n",
            "loss= tensor(0.7070, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.46484375\n",
            "loss= tensor(0.7130, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7108, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7102, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4453125\n",
            "loss= tensor(0.7105, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7104, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7084, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7132, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7136, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7135, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.359375\n",
            "loss= tensor(0.7113, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.375\n",
            "loss= tensor(0.7143, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7101, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.45703125\n",
            "loss= tensor(0.7133, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.36328125\n",
            "loss= tensor(0.7144, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.36328125\n",
            "loss= tensor(0.7101, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.375\n",
            "***********************************************************\n",
            "epoch= 64\n",
            "loss= tensor(0.7089, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7091, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4375\n",
            "loss= tensor(0.7105, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7139, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37890625\n",
            "loss= tensor(0.7140, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37109375\n",
            "loss= tensor(0.7119, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7140, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.35546875\n",
            "loss= tensor(0.7084, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44140625\n",
            "loss= tensor(0.7068, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44921875\n",
            "loss= tensor(0.7118, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7120, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7070, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.453125\n",
            "loss= tensor(0.7148, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3671875\n",
            "loss= tensor(0.7130, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37109375\n",
            "loss= tensor(0.7053, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4453125\n",
            "loss= tensor(0.7141, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.375\n",
            "loss= tensor(0.7117, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7107, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7089, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44921875\n",
            "loss= tensor(0.7140, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7117, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7089, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41796875\n",
            "loss= tensor(0.7108, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7134, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.375\n",
            "loss= tensor(0.7151, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.34375\n",
            "loss= tensor(0.7107, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7104, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7125, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7131, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7104, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7125, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7112, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7115, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7092, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44140625\n",
            "loss= tensor(0.7105, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7131, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7111, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4296875\n",
            "loss= tensor(0.7131, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37890625\n",
            "loss= tensor(0.7128, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7150, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.375\n",
            "***********************************************************\n",
            "epoch= 65\n",
            "loss= tensor(0.7142, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3671875\n",
            "loss= tensor(0.7127, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3671875\n",
            "loss= tensor(0.7133, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7120, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7151, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37109375\n",
            "loss= tensor(0.7083, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.46875\n",
            "loss= tensor(0.7089, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7134, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37890625\n",
            "loss= tensor(0.7149, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.34765625\n",
            "loss= tensor(0.7094, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7113, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7100, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7121, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7117, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7105, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7125, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7133, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7092, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.43359375\n",
            "loss= tensor(0.7124, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7116, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.375\n",
            "loss= tensor(0.7162, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3515625\n",
            "loss= tensor(0.7115, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7095, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7135, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7121, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7148, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.36328125\n",
            "loss= tensor(0.7100, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7131, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37109375\n",
            "loss= tensor(0.7114, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7116, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7112, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7092, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7054, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.46484375\n",
            "loss= tensor(0.7117, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7073, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.453125\n",
            "loss= tensor(0.7097, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44140625\n",
            "loss= tensor(0.7142, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.36328125\n",
            "loss= tensor(0.7075, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4296875\n",
            "loss= tensor(0.7102, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7156, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3125\n",
            "***********************************************************\n",
            "epoch= 66\n",
            "loss= tensor(0.7116, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7126, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7117, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7141, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3671875\n",
            "loss= tensor(0.7157, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.36328125\n",
            "loss= tensor(0.7102, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7071, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4453125\n",
            "loss= tensor(0.7132, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.375\n",
            "loss= tensor(0.7116, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7125, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37109375\n",
            "loss= tensor(0.7076, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41796875\n",
            "loss= tensor(0.7089, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7096, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7137, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7058, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.48046875\n",
            "loss= tensor(0.7118, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7106, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7104, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7102, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7118, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7092, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7144, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7120, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7114, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4296875\n",
            "loss= tensor(0.7109, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7109, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7116, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7099, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.43359375\n",
            "loss= tensor(0.7129, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.375\n",
            "loss= tensor(0.7091, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44140625\n",
            "loss= tensor(0.7106, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4296875\n",
            "loss= tensor(0.7153, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3671875\n",
            "loss= tensor(0.7092, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4453125\n",
            "loss= tensor(0.7164, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3203125\n",
            "loss= tensor(0.7069, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.47265625\n",
            "loss= tensor(0.7064, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44921875\n",
            "loss= tensor(0.7113, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7152, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3671875\n",
            "loss= tensor(0.7154, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3515625\n",
            "loss= tensor(0.7239, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.25\n",
            "***********************************************************\n",
            "epoch= 67\n",
            "loss= tensor(0.7082, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.453125\n",
            "loss= tensor(0.7105, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7126, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7136, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37109375\n",
            "loss= tensor(0.7158, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3515625\n",
            "loss= tensor(0.7113, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7118, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7062, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44140625\n",
            "loss= tensor(0.7178, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3125\n",
            "loss= tensor(0.7112, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7118, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7094, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4296875\n",
            "loss= tensor(0.7139, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7107, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7050, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4765625\n",
            "loss= tensor(0.7093, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41796875\n",
            "loss= tensor(0.7099, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4453125\n",
            "loss= tensor(0.7097, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7109, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7134, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3671875\n",
            "loss= tensor(0.7132, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7113, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7140, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7089, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44921875\n",
            "loss= tensor(0.7122, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37890625\n",
            "loss= tensor(0.7114, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7094, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7109, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7089, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7116, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7104, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4296875\n",
            "loss= tensor(0.7155, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37109375\n",
            "loss= tensor(0.7094, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7052, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4921875\n",
            "loss= tensor(0.7129, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7158, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.35546875\n",
            "loss= tensor(0.7159, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.34765625\n",
            "loss= tensor(0.7090, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.43359375\n",
            "loss= tensor(0.7098, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7099, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4375\n",
            "***********************************************************\n",
            "epoch= 68\n",
            "loss= tensor(0.7108, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7117, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7113, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7157, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37109375\n",
            "loss= tensor(0.7144, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3671875\n",
            "loss= tensor(0.7081, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44140625\n",
            "loss= tensor(0.7109, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7128, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7101, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4375\n",
            "loss= tensor(0.7133, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7104, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7133, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.359375\n",
            "loss= tensor(0.7057, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.46484375\n",
            "loss= tensor(0.7131, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3671875\n",
            "loss= tensor(0.7110, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41796875\n",
            "loss= tensor(0.7106, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7059, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4609375\n",
            "loss= tensor(0.7093, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44921875\n",
            "loss= tensor(0.7132, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7115, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4375\n",
            "loss= tensor(0.7101, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7118, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7143, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7117, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7102, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7128, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.35546875\n",
            "loss= tensor(0.7119, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7079, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.43359375\n",
            "loss= tensor(0.7111, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7151, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.34375\n",
            "loss= tensor(0.7109, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7073, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44921875\n",
            "loss= tensor(0.7107, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7093, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7097, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7122, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7117, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7151, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37109375\n",
            "loss= tensor(0.7145, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7121, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4375\n",
            "***********************************************************\n",
            "epoch= 69\n",
            "loss= tensor(0.7110, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7125, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7101, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41796875\n",
            "loss= tensor(0.7120, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7079, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.453125\n",
            "loss= tensor(0.7061, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4609375\n",
            "loss= tensor(0.7151, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.34765625\n",
            "loss= tensor(0.7103, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7132, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7087, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4296875\n",
            "loss= tensor(0.7171, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.33203125\n",
            "loss= tensor(0.7121, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7118, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7146, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7108, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7090, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44921875\n",
            "loss= tensor(0.7139, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.375\n",
            "loss= tensor(0.7147, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.34765625\n",
            "loss= tensor(0.7088, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.43359375\n",
            "loss= tensor(0.7110, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7121, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7124, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7137, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7110, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7139, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37109375\n",
            "loss= tensor(0.7111, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7117, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7071, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.45703125\n",
            "loss= tensor(0.7084, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4296875\n",
            "loss= tensor(0.7139, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7082, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.43359375\n",
            "loss= tensor(0.7117, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7109, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7111, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7125, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7104, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41796875\n",
            "loss= tensor(0.7089, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4375\n",
            "loss= tensor(0.7081, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7108, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7169, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3125\n",
            "***********************************************************\n",
            "epoch= 70\n",
            "loss= tensor(0.7106, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.43359375\n",
            "loss= tensor(0.7068, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44921875\n",
            "loss= tensor(0.7163, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.33203125\n",
            "loss= tensor(0.7098, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7124, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7123, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7114, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7128, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.375\n",
            "loss= tensor(0.7106, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7106, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7100, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7071, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4453125\n",
            "loss= tensor(0.7097, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7123, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7125, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.375\n",
            "loss= tensor(0.7111, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41796875\n",
            "loss= tensor(0.7142, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3671875\n",
            "loss= tensor(0.7113, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7107, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41796875\n",
            "loss= tensor(0.7115, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.43359375\n",
            "loss= tensor(0.7155, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3515625\n",
            "loss= tensor(0.7091, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7102, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7123, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7134, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7082, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44921875\n",
            "loss= tensor(0.7134, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.34765625\n",
            "loss= tensor(0.7155, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3671875\n",
            "loss= tensor(0.7113, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4296875\n",
            "loss= tensor(0.7112, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7116, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7115, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7109, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41796875\n",
            "loss= tensor(0.7103, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7118, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.375\n",
            "loss= tensor(0.7071, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.43359375\n",
            "loss= tensor(0.7124, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41796875\n",
            "loss= tensor(0.7130, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7078, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.43359375\n",
            "loss= tensor(0.7246, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.1875\n",
            "***********************************************************\n",
            "epoch= 71\n",
            "loss= tensor(0.7104, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7089, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4375\n",
            "loss= tensor(0.7122, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7120, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7132, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7122, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.375\n",
            "loss= tensor(0.7142, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7087, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7073, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4296875\n",
            "loss= tensor(0.7118, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7092, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7095, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44140625\n",
            "loss= tensor(0.7086, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4375\n",
            "loss= tensor(0.7136, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.36328125\n",
            "loss= tensor(0.7152, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.34765625\n",
            "loss= tensor(0.7084, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44140625\n",
            "loss= tensor(0.7141, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37109375\n",
            "loss= tensor(0.7101, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7110, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7136, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.35546875\n",
            "loss= tensor(0.7069, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4609375\n",
            "loss= tensor(0.7132, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7099, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7139, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7109, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41796875\n",
            "loss= tensor(0.7048, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.453125\n",
            "loss= tensor(0.7124, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7099, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7112, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7110, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7073, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44921875\n",
            "loss= tensor(0.7134, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37109375\n",
            "loss= tensor(0.7115, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7078, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4609375\n",
            "loss= tensor(0.7110, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7135, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3671875\n",
            "loss= tensor(0.7145, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.36328125\n",
            "loss= tensor(0.7166, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.34375\n",
            "loss= tensor(0.7113, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41796875\n",
            "loss= tensor(0.7121, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4375\n",
            "***********************************************************\n",
            "epoch= 72\n",
            "loss= tensor(0.7095, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4375\n",
            "loss= tensor(0.7122, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7095, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44140625\n",
            "loss= tensor(0.7147, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7098, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.43359375\n",
            "loss= tensor(0.7093, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4296875\n",
            "loss= tensor(0.7105, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7086, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7110, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7118, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7081, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.453125\n",
            "loss= tensor(0.7138, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7060, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.45703125\n",
            "loss= tensor(0.7095, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7117, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7110, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7118, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7106, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41796875\n",
            "loss= tensor(0.7095, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7118, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7071, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.43359375\n",
            "loss= tensor(0.7129, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7152, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.359375\n",
            "loss= tensor(0.7101, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7092, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7131, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7121, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.43359375\n",
            "loss= tensor(0.7129, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7113, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7113, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7084, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41796875\n",
            "loss= tensor(0.7146, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37109375\n",
            "loss= tensor(0.7132, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.375\n",
            "loss= tensor(0.7160, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.36328125\n",
            "loss= tensor(0.7104, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7110, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41796875\n",
            "loss= tensor(0.7154, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.359375\n",
            "loss= tensor(0.7128, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37109375\n",
            "loss= tensor(0.7106, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.43359375\n",
            "loss= tensor(0.7045, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4375\n",
            "***********************************************************\n",
            "epoch= 73\n",
            "loss= tensor(0.7106, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.43359375\n",
            "loss= tensor(0.7099, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.43359375\n",
            "loss= tensor(0.7140, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7088, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7146, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37890625\n",
            "loss= tensor(0.7093, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7078, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.45703125\n",
            "loss= tensor(0.7056, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.46875\n",
            "loss= tensor(0.7116, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7087, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7090, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4296875\n",
            "loss= tensor(0.7085, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44140625\n",
            "loss= tensor(0.7116, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7109, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41796875\n",
            "loss= tensor(0.7098, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7121, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7128, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.36328125\n",
            "loss= tensor(0.7094, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7124, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7123, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7136, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3671875\n",
            "loss= tensor(0.7114, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4375\n",
            "loss= tensor(0.7075, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.47265625\n",
            "loss= tensor(0.7092, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7122, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7153, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.35546875\n",
            "loss= tensor(0.7127, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7184, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.296875\n",
            "loss= tensor(0.7104, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7125, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37890625\n",
            "loss= tensor(0.7157, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.33984375\n",
            "loss= tensor(0.7102, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7087, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44140625\n",
            "loss= tensor(0.7113, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7101, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7104, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7134, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.375\n",
            "loss= tensor(0.7082, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7133, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37109375\n",
            "loss= tensor(0.7046, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.5\n",
            "***********************************************************\n",
            "epoch= 74\n",
            "loss= tensor(0.7095, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4296875\n",
            "loss= tensor(0.7153, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3671875\n",
            "loss= tensor(0.7101, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41796875\n",
            "loss= tensor(0.7099, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4296875\n",
            "loss= tensor(0.7130, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7130, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.359375\n",
            "loss= tensor(0.7075, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7113, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7131, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37890625\n",
            "loss= tensor(0.7102, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7118, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7134, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7086, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7098, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7120, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7120, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7125, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.359375\n",
            "loss= tensor(0.7110, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7133, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7111, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7132, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7136, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7150, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.375\n",
            "loss= tensor(0.7127, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7120, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7106, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7082, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.43359375\n",
            "loss= tensor(0.7128, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7106, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7078, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.45703125\n",
            "loss= tensor(0.7114, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7091, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7124, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7094, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44921875\n",
            "loss= tensor(0.7120, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7066, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.46484375\n",
            "loss= tensor(0.7087, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.43359375\n",
            "loss= tensor(0.7100, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7130, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7061, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4375\n",
            "***********************************************************\n",
            "epoch= 75\n",
            "loss= tensor(0.7079, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44921875\n",
            "loss= tensor(0.7101, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41796875\n",
            "loss= tensor(0.7100, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7084, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4375\n",
            "loss= tensor(0.7145, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.359375\n",
            "loss= tensor(0.7122, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7100, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7128, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7117, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.375\n",
            "loss= tensor(0.7125, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7110, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7102, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4375\n",
            "loss= tensor(0.7087, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7124, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7055, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4765625\n",
            "loss= tensor(0.7093, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4296875\n",
            "loss= tensor(0.7094, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44140625\n",
            "loss= tensor(0.7120, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7094, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44140625\n",
            "loss= tensor(0.7103, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7066, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44921875\n",
            "loss= tensor(0.7107, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7117, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7151, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37890625\n",
            "loss= tensor(0.7103, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7097, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.43359375\n",
            "loss= tensor(0.7087, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7172, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.33203125\n",
            "loss= tensor(0.7125, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7140, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.359375\n",
            "loss= tensor(0.7147, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.35546875\n",
            "loss= tensor(0.7162, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.359375\n",
            "loss= tensor(0.7102, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7080, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44921875\n",
            "loss= tensor(0.7102, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7122, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37890625\n",
            "loss= tensor(0.7127, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7091, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7154, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7120, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3125\n",
            "***********************************************************\n",
            "epoch= 76\n",
            "loss= tensor(0.7109, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41796875\n",
            "loss= tensor(0.7132, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7090, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4296875\n",
            "loss= tensor(0.7099, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7091, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7104, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44140625\n",
            "loss= tensor(0.7094, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7128, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7112, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7086, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44140625\n",
            "loss= tensor(0.7102, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7145, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.359375\n",
            "loss= tensor(0.7115, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7105, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7119, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7107, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41796875\n",
            "loss= tensor(0.7094, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44140625\n",
            "loss= tensor(0.7142, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37109375\n",
            "loss= tensor(0.7101, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.43359375\n",
            "loss= tensor(0.7147, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.33984375\n",
            "loss= tensor(0.7054, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4453125\n",
            "loss= tensor(0.7110, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7095, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4453125\n",
            "loss= tensor(0.7135, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7118, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7055, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.484375\n",
            "loss= tensor(0.7088, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7145, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3515625\n",
            "loss= tensor(0.7137, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.35546875\n",
            "loss= tensor(0.7093, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7151, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.36328125\n",
            "loss= tensor(0.7106, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7117, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7097, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7127, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37109375\n",
            "loss= tensor(0.7125, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7114, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4375\n",
            "loss= tensor(0.7113, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7115, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7112, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.375\n",
            "***********************************************************\n",
            "epoch= 77\n",
            "loss= tensor(0.7127, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7160, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.34765625\n",
            "loss= tensor(0.7113, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4296875\n",
            "loss= tensor(0.7118, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7095, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7098, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7079, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.43359375\n",
            "loss= tensor(0.7133, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37109375\n",
            "loss= tensor(0.7106, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7056, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.453125\n",
            "loss= tensor(0.7098, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41796875\n",
            "loss= tensor(0.7100, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7086, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.43359375\n",
            "loss= tensor(0.7093, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7083, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.43359375\n",
            "loss= tensor(0.7101, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7132, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7107, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7132, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7095, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.43359375\n",
            "loss= tensor(0.7067, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.45703125\n",
            "loss= tensor(0.7075, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44140625\n",
            "loss= tensor(0.7127, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7108, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7114, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7032, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4921875\n",
            "loss= tensor(0.7148, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37109375\n",
            "loss= tensor(0.7097, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7127, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7133, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7178, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3359375\n",
            "loss= tensor(0.7123, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7129, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7140, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7102, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7106, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7105, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7155, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.359375\n",
            "loss= tensor(0.7093, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4296875\n",
            "loss= tensor(0.7094, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.5625\n",
            "***********************************************************\n",
            "epoch= 78\n",
            "loss= tensor(0.7082, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.453125\n",
            "loss= tensor(0.7136, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.359375\n",
            "loss= tensor(0.7115, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41796875\n",
            "loss= tensor(0.7121, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7130, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.375\n",
            "loss= tensor(0.7156, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.359375\n",
            "loss= tensor(0.7114, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7044, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.47265625\n",
            "loss= tensor(0.7145, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7052, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.453125\n",
            "loss= tensor(0.7133, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7132, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37109375\n",
            "loss= tensor(0.7054, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4609375\n",
            "loss= tensor(0.7082, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.453125\n",
            "loss= tensor(0.7110, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7099, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44140625\n",
            "loss= tensor(0.7136, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37109375\n",
            "loss= tensor(0.7084, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41796875\n",
            "loss= tensor(0.7165, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.35546875\n",
            "loss= tensor(0.7117, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7097, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7105, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7108, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41796875\n",
            "loss= tensor(0.7149, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37890625\n",
            "loss= tensor(0.7087, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.45703125\n",
            "loss= tensor(0.7119, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.375\n",
            "loss= tensor(0.7093, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.43359375\n",
            "loss= tensor(0.7107, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7111, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.43359375\n",
            "loss= tensor(0.7108, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4296875\n",
            "loss= tensor(0.7067, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4375\n",
            "loss= tensor(0.7112, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7111, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7150, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.36328125\n",
            "loss= tensor(0.7126, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37109375\n",
            "loss= tensor(0.7096, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7124, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7129, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7128, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7215, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.375\n",
            "***********************************************************\n",
            "epoch= 79\n",
            "loss= tensor(0.7161, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.34375\n",
            "loss= tensor(0.7089, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.43359375\n",
            "loss= tensor(0.7099, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7093, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7152, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3671875\n",
            "loss= tensor(0.7120, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7120, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7087, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.43359375\n",
            "loss= tensor(0.7128, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7102, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7093, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7067, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4609375\n",
            "loss= tensor(0.7116, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7102, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7133, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7147, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7107, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7130, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7065, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.45703125\n",
            "loss= tensor(0.7064, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4609375\n",
            "loss= tensor(0.7109, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4296875\n",
            "loss= tensor(0.7141, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7098, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7097, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7115, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7126, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.359375\n",
            "loss= tensor(0.7074, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.453125\n",
            "loss= tensor(0.7100, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44921875\n",
            "loss= tensor(0.7102, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7118, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7106, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7112, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7102, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7129, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7128, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7073, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.47265625\n",
            "loss= tensor(0.7075, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7133, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7129, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.6943, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.75\n",
            "***********************************************************\n",
            "epoch= 80\n",
            "loss= tensor(0.7108, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41796875\n",
            "loss= tensor(0.7071, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.46484375\n",
            "loss= tensor(0.7096, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7096, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7146, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.359375\n",
            "loss= tensor(0.7106, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7127, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7066, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4453125\n",
            "loss= tensor(0.7131, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7152, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7115, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7107, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7143, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37890625\n",
            "loss= tensor(0.7142, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.36328125\n",
            "loss= tensor(0.7132, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7101, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7087, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44921875\n",
            "loss= tensor(0.7124, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37109375\n",
            "loss= tensor(0.7097, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7108, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7154, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.359375\n",
            "loss= tensor(0.7111, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7139, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.35546875\n",
            "loss= tensor(0.7092, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7097, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7109, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7113, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41796875\n",
            "loss= tensor(0.7095, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.43359375\n",
            "loss= tensor(0.7117, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7141, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7098, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7111, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7110, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7122, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7134, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7071, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4453125\n",
            "loss= tensor(0.7068, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.453125\n",
            "loss= tensor(0.7110, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7060, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.46484375\n",
            "loss= tensor(0.7082, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4375\n",
            "***********************************************************\n",
            "epoch= 81\n",
            "loss= tensor(0.7087, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4375\n",
            "loss= tensor(0.7108, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7106, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7084, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44921875\n",
            "loss= tensor(0.7082, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.46875\n",
            "loss= tensor(0.7139, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.359375\n",
            "loss= tensor(0.7118, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7109, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7095, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4375\n",
            "loss= tensor(0.7090, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4296875\n",
            "loss= tensor(0.7094, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41796875\n",
            "loss= tensor(0.7117, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7053, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4765625\n",
            "loss= tensor(0.7152, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.35546875\n",
            "loss= tensor(0.7087, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7131, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3671875\n",
            "loss= tensor(0.7092, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7082, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4609375\n",
            "loss= tensor(0.7089, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7092, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7130, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7110, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7140, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7107, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7089, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4296875\n",
            "loss= tensor(0.7093, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7133, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7111, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7129, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7134, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7108, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7137, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.375\n",
            "loss= tensor(0.7130, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37890625\n",
            "loss= tensor(0.7134, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7141, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37109375\n",
            "loss= tensor(0.7115, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7060, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.47265625\n",
            "loss= tensor(0.7130, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7107, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7211, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.25\n",
            "***********************************************************\n",
            "epoch= 82\n",
            "loss= tensor(0.7051, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44921875\n",
            "loss= tensor(0.7137, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7094, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4375\n",
            "loss= tensor(0.7122, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7091, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4453125\n",
            "loss= tensor(0.7138, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.375\n",
            "loss= tensor(0.7093, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7083, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7131, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.36328125\n",
            "loss= tensor(0.7046, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.48828125\n",
            "loss= tensor(0.7113, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7094, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41796875\n",
            "loss= tensor(0.7129, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7099, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7093, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41796875\n",
            "loss= tensor(0.7120, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7118, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7129, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7106, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7121, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7072, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.45703125\n",
            "loss= tensor(0.7076, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4375\n",
            "loss= tensor(0.7104, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44921875\n",
            "loss= tensor(0.7166, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.34375\n",
            "loss= tensor(0.7131, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7134, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7123, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7129, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37109375\n",
            "loss= tensor(0.7091, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7087, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.46484375\n",
            "loss= tensor(0.7121, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7125, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7096, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.43359375\n",
            "loss= tensor(0.7109, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7092, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41796875\n",
            "loss= tensor(0.7127, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37890625\n",
            "loss= tensor(0.7136, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.375\n",
            "loss= tensor(0.7131, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7131, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7122, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4375\n",
            "***********************************************************\n",
            "epoch= 83\n",
            "loss= tensor(0.7122, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37890625\n",
            "loss= tensor(0.7102, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7097, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7129, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7114, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7113, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7098, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7095, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4375\n",
            "loss= tensor(0.7119, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7103, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7144, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3671875\n",
            "loss= tensor(0.7065, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.453125\n",
            "loss= tensor(0.7122, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37890625\n",
            "loss= tensor(0.7116, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7124, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7069, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44140625\n",
            "loss= tensor(0.7114, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7145, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37109375\n",
            "loss= tensor(0.7092, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.43359375\n",
            "loss= tensor(0.7080, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7111, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7148, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7104, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41796875\n",
            "loss= tensor(0.7162, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.35546875\n",
            "loss= tensor(0.7117, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7111, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7081, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44140625\n",
            "loss= tensor(0.7109, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7126, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7141, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37890625\n",
            "loss= tensor(0.7137, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3671875\n",
            "loss= tensor(0.7106, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4296875\n",
            "loss= tensor(0.7047, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.45703125\n",
            "loss= tensor(0.7086, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7128, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37109375\n",
            "loss= tensor(0.7110, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7086, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.48046875\n",
            "loss= tensor(0.7146, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3671875\n",
            "loss= tensor(0.7109, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.6929, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.625\n",
            "***********************************************************\n",
            "epoch= 84\n",
            "loss= tensor(0.7130, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7051, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.453125\n",
            "loss= tensor(0.7129, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7141, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37109375\n",
            "loss= tensor(0.7150, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7106, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7107, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7114, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.375\n",
            "loss= tensor(0.7116, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7140, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.375\n",
            "loss= tensor(0.7096, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7119, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7112, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7155, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7106, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41796875\n",
            "loss= tensor(0.7071, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.45703125\n",
            "loss= tensor(0.7119, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7119, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7127, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41796875\n",
            "loss= tensor(0.7101, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7119, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7076, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44140625\n",
            "loss= tensor(0.7104, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7141, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7087, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7121, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7112, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7094, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7079, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44921875\n",
            "loss= tensor(0.7095, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7079, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.43359375\n",
            "loss= tensor(0.7139, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7136, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37109375\n",
            "loss= tensor(0.7137, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3671875\n",
            "loss= tensor(0.7099, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.43359375\n",
            "loss= tensor(0.7114, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7078, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.43359375\n",
            "loss= tensor(0.7106, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7077, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.46484375\n",
            "loss= tensor(0.7044, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.5\n",
            "***********************************************************\n",
            "epoch= 85\n",
            "loss= tensor(0.7138, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.36328125\n",
            "loss= tensor(0.7126, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7081, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.46875\n",
            "loss= tensor(0.7138, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37890625\n",
            "loss= tensor(0.7118, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7097, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7125, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.375\n",
            "loss= tensor(0.7069, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.45703125\n",
            "loss= tensor(0.7132, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.36328125\n",
            "loss= tensor(0.7139, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3671875\n",
            "loss= tensor(0.7102, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7119, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7088, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4296875\n",
            "loss= tensor(0.7075, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.43359375\n",
            "loss= tensor(0.7114, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7129, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7110, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7099, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44140625\n",
            "loss= tensor(0.7069, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4609375\n",
            "loss= tensor(0.7101, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7077, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44140625\n",
            "loss= tensor(0.7090, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.43359375\n",
            "loss= tensor(0.7099, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7134, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37890625\n",
            "loss= tensor(0.7108, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7095, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4453125\n",
            "loss= tensor(0.7132, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7117, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7140, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7133, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.375\n",
            "loss= tensor(0.7137, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3515625\n",
            "loss= tensor(0.7123, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7074, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.45703125\n",
            "loss= tensor(0.7085, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41796875\n",
            "loss= tensor(0.7090, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4453125\n",
            "loss= tensor(0.7112, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7114, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7109, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7110, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7086, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.5\n",
            "***********************************************************\n",
            "epoch= 86\n",
            "loss= tensor(0.7106, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4296875\n",
            "loss= tensor(0.7074, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4453125\n",
            "loss= tensor(0.7096, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7115, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7071, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44921875\n",
            "loss= tensor(0.7111, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7078, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.47265625\n",
            "loss= tensor(0.7169, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.34765625\n",
            "loss= tensor(0.7111, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41796875\n",
            "loss= tensor(0.7084, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4296875\n",
            "loss= tensor(0.7117, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37890625\n",
            "loss= tensor(0.7145, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7119, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7149, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.36328125\n",
            "loss= tensor(0.7164, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3359375\n",
            "loss= tensor(0.7072, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44921875\n",
            "loss= tensor(0.7110, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7123, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7093, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7081, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44140625\n",
            "loss= tensor(0.7133, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7123, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7147, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.359375\n",
            "loss= tensor(0.7094, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7138, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3515625\n",
            "loss= tensor(0.7061, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.48046875\n",
            "loss= tensor(0.7147, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3515625\n",
            "loss= tensor(0.7112, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7107, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7101, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4296875\n",
            "loss= tensor(0.7099, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41796875\n",
            "loss= tensor(0.7114, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7110, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7104, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41796875\n",
            "loss= tensor(0.7122, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37109375\n",
            "loss= tensor(0.7109, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7109, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7088, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4375\n",
            "loss= tensor(0.7095, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41796875\n",
            "loss= tensor(0.7019, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.625\n",
            "***********************************************************\n",
            "epoch= 87\n",
            "loss= tensor(0.7084, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7126, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41796875\n",
            "loss= tensor(0.7145, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7114, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7055, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44921875\n",
            "loss= tensor(0.7092, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4375\n",
            "loss= tensor(0.7098, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7072, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4375\n",
            "loss= tensor(0.7135, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7085, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4296875\n",
            "loss= tensor(0.7095, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7149, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7066, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.47265625\n",
            "loss= tensor(0.7073, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4453125\n",
            "loss= tensor(0.7068, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.45703125\n",
            "loss= tensor(0.7143, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.359375\n",
            "loss= tensor(0.7150, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3515625\n",
            "loss= tensor(0.7113, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7134, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37890625\n",
            "loss= tensor(0.7065, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.453125\n",
            "loss= tensor(0.7107, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7081, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44921875\n",
            "loss= tensor(0.7112, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7101, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7126, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7082, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4453125\n",
            "loss= tensor(0.7109, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7087, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41796875\n",
            "loss= tensor(0.7105, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41796875\n",
            "loss= tensor(0.7118, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7113, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7101, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7121, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7123, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37890625\n",
            "loss= tensor(0.7120, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7135, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37890625\n",
            "loss= tensor(0.7160, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.35546875\n",
            "loss= tensor(0.7164, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.35546875\n",
            "loss= tensor(0.7096, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4296875\n",
            "loss= tensor(0.7119, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4375\n",
            "***********************************************************\n",
            "epoch= 88\n",
            "loss= tensor(0.7060, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4765625\n",
            "loss= tensor(0.7098, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7154, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.33984375\n",
            "loss= tensor(0.7081, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4453125\n",
            "loss= tensor(0.7122, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7117, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7096, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.43359375\n",
            "loss= tensor(0.7071, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4609375\n",
            "loss= tensor(0.7131, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7077, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4609375\n",
            "loss= tensor(0.7051, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.47265625\n",
            "loss= tensor(0.7105, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7084, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7159, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3515625\n",
            "loss= tensor(0.7082, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44921875\n",
            "loss= tensor(0.7143, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.34765625\n",
            "loss= tensor(0.7099, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7078, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44140625\n",
            "loss= tensor(0.7079, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44921875\n",
            "loss= tensor(0.7134, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7093, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7080, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.43359375\n",
            "loss= tensor(0.7138, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7085, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.43359375\n",
            "loss= tensor(0.7085, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4453125\n",
            "loss= tensor(0.7132, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.375\n",
            "loss= tensor(0.7149, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3515625\n",
            "loss= tensor(0.7154, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37109375\n",
            "loss= tensor(0.7139, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.375\n",
            "loss= tensor(0.7169, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3515625\n",
            "loss= tensor(0.7109, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7149, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3515625\n",
            "loss= tensor(0.7099, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4296875\n",
            "loss= tensor(0.7103, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7101, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7094, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7088, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.46484375\n",
            "loss= tensor(0.7167, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.34375\n",
            "loss= tensor(0.7103, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7162, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.375\n",
            "***********************************************************\n",
            "epoch= 89\n",
            "loss= tensor(0.7133, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7118, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7072, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4296875\n",
            "loss= tensor(0.7114, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7062, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.46484375\n",
            "loss= tensor(0.7152, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7127, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7085, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4375\n",
            "loss= tensor(0.7117, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7105, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4296875\n",
            "loss= tensor(0.7114, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7119, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7142, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.359375\n",
            "loss= tensor(0.7137, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3515625\n",
            "loss= tensor(0.7117, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37109375\n",
            "loss= tensor(0.7092, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.43359375\n",
            "loss= tensor(0.7066, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44921875\n",
            "loss= tensor(0.7130, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7090, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7064, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4609375\n",
            "loss= tensor(0.7126, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7125, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7150, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.33984375\n",
            "loss= tensor(0.7108, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7102, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7156, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.34765625\n",
            "loss= tensor(0.7121, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7047, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.47265625\n",
            "loss= tensor(0.7104, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7075, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44921875\n",
            "loss= tensor(0.7084, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4375\n",
            "loss= tensor(0.7128, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37890625\n",
            "loss= tensor(0.7099, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4296875\n",
            "loss= tensor(0.7101, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7087, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4296875\n",
            "loss= tensor(0.7096, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7104, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7111, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41796875\n",
            "loss= tensor(0.7115, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7188, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3125\n",
            "***********************************************************\n",
            "epoch= 90\n",
            "loss= tensor(0.7130, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7114, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7098, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4453125\n",
            "loss= tensor(0.7097, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7099, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41796875\n",
            "loss= tensor(0.7046, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.49609375\n",
            "loss= tensor(0.7070, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.43359375\n",
            "loss= tensor(0.7111, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7136, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37109375\n",
            "loss= tensor(0.7117, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7092, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7107, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7072, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4375\n",
            "loss= tensor(0.7129, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37109375\n",
            "loss= tensor(0.7107, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44140625\n",
            "loss= tensor(0.7105, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4296875\n",
            "loss= tensor(0.7109, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41796875\n",
            "loss= tensor(0.7090, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4375\n",
            "loss= tensor(0.7111, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7100, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7094, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4375\n",
            "loss= tensor(0.7120, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7096, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7131, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7114, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7146, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3671875\n",
            "loss= tensor(0.7083, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4375\n",
            "loss= tensor(0.7126, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7113, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7141, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.36328125\n",
            "loss= tensor(0.7114, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.375\n",
            "loss= tensor(0.7144, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7125, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37890625\n",
            "loss= tensor(0.7125, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7120, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7091, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44140625\n",
            "loss= tensor(0.7092, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7122, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3671875\n",
            "loss= tensor(0.7096, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7163, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.375\n",
            "***********************************************************\n",
            "epoch= 91\n",
            "loss= tensor(0.7122, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7114, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7111, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7109, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7159, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.35546875\n",
            "loss= tensor(0.7060, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.46875\n",
            "loss= tensor(0.7080, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7098, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7080, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.453125\n",
            "loss= tensor(0.7105, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7133, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7133, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7121, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7094, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7097, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41796875\n",
            "loss= tensor(0.7108, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7097, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41796875\n",
            "loss= tensor(0.7147, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7101, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7098, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4296875\n",
            "loss= tensor(0.7078, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.45703125\n",
            "loss= tensor(0.7106, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7099, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7056, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.46484375\n",
            "loss= tensor(0.7111, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7122, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7072, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44921875\n",
            "loss= tensor(0.7130, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37109375\n",
            "loss= tensor(0.7140, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.36328125\n",
            "loss= tensor(0.7111, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7116, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.375\n",
            "loss= tensor(0.7083, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4375\n",
            "loss= tensor(0.7106, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7115, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7096, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7158, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37890625\n",
            "loss= tensor(0.7115, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7108, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7115, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7219, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.1875\n",
            "***********************************************************\n",
            "epoch= 92\n",
            "loss= tensor(0.7109, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7108, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.375\n",
            "loss= tensor(0.7105, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7083, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7096, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.43359375\n",
            "loss= tensor(0.7063, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4609375\n",
            "loss= tensor(0.7121, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7077, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.45703125\n",
            "loss= tensor(0.7117, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7080, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44921875\n",
            "loss= tensor(0.7078, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7089, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4453125\n",
            "loss= tensor(0.7071, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44140625\n",
            "loss= tensor(0.7134, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7107, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7127, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7150, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7125, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7066, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.453125\n",
            "loss= tensor(0.7123, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37890625\n",
            "loss= tensor(0.7121, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4296875\n",
            "loss= tensor(0.7144, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3671875\n",
            "loss= tensor(0.7115, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7115, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7103, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7123, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7074, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4375\n",
            "loss= tensor(0.7077, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.45703125\n",
            "loss= tensor(0.7081, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7100, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4453125\n",
            "loss= tensor(0.7150, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.36328125\n",
            "loss= tensor(0.7154, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.34765625\n",
            "loss= tensor(0.7097, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7078, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7100, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7162, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37109375\n",
            "loss= tensor(0.7112, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7123, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7128, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7120, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.375\n",
            "***********************************************************\n",
            "epoch= 93\n",
            "loss= tensor(0.7166, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.328125\n",
            "loss= tensor(0.7108, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7133, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7126, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7124, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3671875\n",
            "loss= tensor(0.7071, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.453125\n",
            "loss= tensor(0.7109, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7057, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4609375\n",
            "loss= tensor(0.7039, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.5\n",
            "loss= tensor(0.7096, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44921875\n",
            "loss= tensor(0.7121, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7116, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7108, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7102, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7125, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.36328125\n",
            "loss= tensor(0.7104, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7121, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7084, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.453125\n",
            "loss= tensor(0.7140, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.359375\n",
            "loss= tensor(0.7146, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.34765625\n",
            "loss= tensor(0.7112, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7138, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7055, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4765625\n",
            "loss= tensor(0.7094, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4296875\n",
            "loss= tensor(0.7070, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.46484375\n",
            "loss= tensor(0.7064, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.45703125\n",
            "loss= tensor(0.7093, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7086, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44140625\n",
            "loss= tensor(0.7125, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7097, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4375\n",
            "loss= tensor(0.7128, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7125, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7130, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.36328125\n",
            "loss= tensor(0.7106, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7086, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7158, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.34375\n",
            "loss= tensor(0.7103, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7089, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4375\n",
            "loss= tensor(0.7123, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.6966, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.5625\n",
            "***********************************************************\n",
            "epoch= 94\n",
            "loss= tensor(0.7120, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7156, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.34765625\n",
            "loss= tensor(0.7069, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.453125\n",
            "loss= tensor(0.7098, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7124, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7092, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4375\n",
            "loss= tensor(0.7048, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4609375\n",
            "loss= tensor(0.7088, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7094, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7106, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41796875\n",
            "loss= tensor(0.7121, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7082, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44140625\n",
            "loss= tensor(0.7132, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.34765625\n",
            "loss= tensor(0.7115, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7129, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7081, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4609375\n",
            "loss= tensor(0.7120, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7064, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.46484375\n",
            "loss= tensor(0.7120, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7147, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.35546875\n",
            "loss= tensor(0.7049, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.5\n",
            "loss= tensor(0.7126, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7128, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7128, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37109375\n",
            "loss= tensor(0.7092, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7113, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7133, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7118, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7105, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7121, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7089, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7087, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7087, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4296875\n",
            "loss= tensor(0.7123, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7124, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.375\n",
            "loss= tensor(0.7119, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7113, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44140625\n",
            "loss= tensor(0.7112, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7080, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4375\n",
            "loss= tensor(0.7267, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.1875\n",
            "***********************************************************\n",
            "epoch= 95\n",
            "loss= tensor(0.7126, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7120, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7109, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7122, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37109375\n",
            "loss= tensor(0.7133, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7103, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7108, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7122, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7118, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7100, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7115, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7097, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7132, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7114, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44140625\n",
            "loss= tensor(0.7082, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.453125\n",
            "loss= tensor(0.7053, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4765625\n",
            "loss= tensor(0.7149, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.35546875\n",
            "loss= tensor(0.7117, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7088, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44140625\n",
            "loss= tensor(0.7111, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4375\n",
            "loss= tensor(0.7103, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7122, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7107, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7125, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7100, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7031, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.5\n",
            "loss= tensor(0.7114, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7110, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7112, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41796875\n",
            "loss= tensor(0.7089, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4453125\n",
            "loss= tensor(0.7120, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7112, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37890625\n",
            "loss= tensor(0.7106, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7110, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7099, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41796875\n",
            "loss= tensor(0.7109, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7079, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4375\n",
            "loss= tensor(0.7105, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7090, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4453125\n",
            "loss= tensor(0.7331, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.125\n",
            "***********************************************************\n",
            "epoch= 96\n",
            "loss= tensor(0.7115, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7087, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4609375\n",
            "loss= tensor(0.7136, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37890625\n",
            "loss= tensor(0.7124, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7131, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3671875\n",
            "loss= tensor(0.7095, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7136, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37890625\n",
            "loss= tensor(0.7155, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.36328125\n",
            "loss= tensor(0.7066, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.48046875\n",
            "loss= tensor(0.7077, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41796875\n",
            "loss= tensor(0.7089, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44140625\n",
            "loss= tensor(0.7150, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.34375\n",
            "loss= tensor(0.7086, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44140625\n",
            "loss= tensor(0.7106, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7110, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7064, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44140625\n",
            "loss= tensor(0.7095, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7126, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7074, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44140625\n",
            "loss= tensor(0.7123, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3671875\n",
            "loss= tensor(0.7139, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.34375\n",
            "loss= tensor(0.7093, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7105, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7118, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37890625\n",
            "loss= tensor(0.7076, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.45703125\n",
            "loss= tensor(0.7113, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7097, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4453125\n",
            "loss= tensor(0.7136, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7128, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7120, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7110, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44140625\n",
            "loss= tensor(0.7134, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7104, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7144, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.375\n",
            "loss= tensor(0.7072, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4453125\n",
            "loss= tensor(0.7103, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7119, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7068, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.453125\n",
            "loss= tensor(0.7085, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4453125\n",
            "loss= tensor(0.7152, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3125\n",
            "***********************************************************\n",
            "epoch= 97\n",
            "loss= tensor(0.7078, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4375\n",
            "loss= tensor(0.7110, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7087, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.45703125\n",
            "loss= tensor(0.7101, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7070, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44140625\n",
            "loss= tensor(0.7083, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41796875\n",
            "loss= tensor(0.7104, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4453125\n",
            "loss= tensor(0.7111, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.43359375\n",
            "loss= tensor(0.7107, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7118, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7062, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.48046875\n",
            "loss= tensor(0.7088, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7123, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7083, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4375\n",
            "loss= tensor(0.7098, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7137, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37890625\n",
            "loss= tensor(0.7150, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37109375\n",
            "loss= tensor(0.7119, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7131, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7108, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7060, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4453125\n",
            "loss= tensor(0.7111, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7129, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.359375\n",
            "loss= tensor(0.7115, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7134, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7106, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7067, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4765625\n",
            "loss= tensor(0.7085, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7118, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7151, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3515625\n",
            "loss= tensor(0.7110, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7121, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7126, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7126, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7119, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7113, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7110, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7077, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44921875\n",
            "loss= tensor(0.7111, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7143, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.375\n",
            "***********************************************************\n",
            "epoch= 98\n",
            "loss= tensor(0.7078, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4453125\n",
            "loss= tensor(0.7142, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.375\n",
            "loss= tensor(0.7056, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4609375\n",
            "loss= tensor(0.7108, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7138, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37890625\n",
            "loss= tensor(0.7118, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7127, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7120, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7098, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7104, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7141, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7107, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7109, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.375\n",
            "loss= tensor(0.7108, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7105, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40234375\n",
            "loss= tensor(0.7180, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.31640625\n",
            "loss= tensor(0.7139, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7092, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7082, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4609375\n",
            "loss= tensor(0.7113, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7122, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7111, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7071, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44140625\n",
            "loss= tensor(0.7100, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7096, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7144, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.36328125\n",
            "loss= tensor(0.7096, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4375\n",
            "loss= tensor(0.7112, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7086, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4375\n",
            "loss= tensor(0.7110, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7106, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.390625\n",
            "loss= tensor(0.7151, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7114, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7052, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.46875\n",
            "loss= tensor(0.7080, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.43359375\n",
            "loss= tensor(0.7087, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7084, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4453125\n",
            "loss= tensor(0.7071, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.45703125\n",
            "loss= tensor(0.7097, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7300, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.1875\n",
            "***********************************************************\n",
            "epoch= 99\n",
            "loss= tensor(0.7130, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3671875\n",
            "loss= tensor(0.7141, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7104, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4296875\n",
            "loss= tensor(0.7099, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7059, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.46484375\n",
            "loss= tensor(0.7072, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4375\n",
            "loss= tensor(0.7122, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3828125\n",
            "loss= tensor(0.7094, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7080, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.46875\n",
            "loss= tensor(0.7096, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7107, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7093, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7119, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37890625\n",
            "loss= tensor(0.7105, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7122, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.38671875\n",
            "loss= tensor(0.7148, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37890625\n",
            "loss= tensor(0.7117, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7106, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4296875\n",
            "loss= tensor(0.7081, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.42578125\n",
            "loss= tensor(0.7085, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44140625\n",
            "loss= tensor(0.7099, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.41015625\n",
            "loss= tensor(0.7125, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37109375\n",
            "loss= tensor(0.7140, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.359375\n",
            "loss= tensor(0.7112, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.40625\n",
            "loss= tensor(0.7076, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.44921875\n",
            "loss= tensor(0.7137, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3671875\n",
            "loss= tensor(0.7148, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3515625\n",
            "loss= tensor(0.7128, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.37109375\n",
            "loss= tensor(0.7109, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4296875\n",
            "loss= tensor(0.7116, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.39453125\n",
            "loss= tensor(0.7128, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.375\n",
            "loss= tensor(0.7099, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4375\n",
            "loss= tensor(0.7090, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4375\n",
            "loss= tensor(0.7071, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4375\n",
            "loss= tensor(0.7102, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.4140625\n",
            "loss= tensor(0.7079, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.43359375\n",
            "loss= tensor(0.7085, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.43359375\n",
            "loss= tensor(0.7122, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.3984375\n",
            "loss= tensor(0.7110, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.421875\n",
            "loss= tensor(0.7076, device='cuda:0', grad_fn=<MeanBackward0>)\n",
            "accuracy= 0.625\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ctNaG30anqNm",
        "outputId": "1f2f9b2f-9ca2-4898-ff2d-d309f87ea79f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "# 每个epoch的平均loss\n",
        "plt.plot(range(1,len(loss_epochMean_list)+1),loss_epochMean_list)\n",
        "plt.title('loss_mean_epoch')\n",
        "plt.ylabel('loss')\n",
        "plt.show()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V2POjQ4ZntSb",
        "outputId": "3510c3af-5851-4ecb-bbf9-c9a4ee39b372",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        }
      },
      "source": [
        "# 每个epoch的平均accuracy\n",
        "plt.plot(range(1,len(accuracy_mean_list)+1),accuracy_mean_list)\n",
        "plt.title('accuracy_mean_epoch')\n",
        "plt.ylabel('accuracy_mean')\n",
        "plt.show()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VJA963WGop1U"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}